Invited Speakers 

Assoc. Prof. Sasaki Yuya
Osaka University, Japan

 

Talk title: Graph neural network and its application

Abstract:
Graph data is ubiquitous in our society, such as social networks, purchase history, molecules, and knowledge representations.
Graph neural networks are deep learning techniques for graph data to enable us to make accurate predictions in many scenarios, including recommendation systems, chemical and material discovery, and web search.
I will talk here about graph neural networks, how they can be utilized in our society, and the future challenges.
 

Bio: Yuya Sasaki is an associate professor at Osaka University, Japan, who obtained a PhD degree (Information Science) from Osaka University in 2014.
His research focuses on graph data management/analysis, trustworthy AI, and applied data science to other fields such as chemical and medical science.

 

Assoc. Prof. He Li
Muroran Institute of Technology, Japan
 

Bio: He Li received the B.S., M.S. degrees in Computer Science and Engineering from Huazhong University of Science and Technology in 2007 and 2009, respectively, and Ph.D. degree in Computer Science and Engineering from The University of Aizu in 2015. He is currently an Associate Professor with Department of Sciences and Informatics, Muroran Institute of Technology, Japan. In 2018, he is selected as a Ministry of Education, Culture, Sports, Science and Technology (MEXT) Excellent Young Researcher. His research interests include IoT, edge computing, cloud computing and software defined networking. He has received the best journal paper awards from IEEE ComSoc APB and IEEE CSIM, and best paper awards from ICPADS 2019 and IEEE VTC2016-Fall. Dr. Li serves as an Associate Editor for Human-centric Computing and Information Sciences (HCIS), as well as Guest Associate Editors for Security and Communication Networks ,Environments, and IEICE Transactions on Information and Systems. He is the recipient of 2019 IEEE TCSC Award for Excellence (Early Career Researcher) and 2016 IEEE TCSC Award for Excellence (Outstanding Ph.D Thesis).